A new method is presented for the identification of systems parameterized bylinear state-space models. The method relies on the concept of subspacefitting, wherein an input/output data model parameterized by the statematrices is found that best fits, in the least-squares sense, the dominantsubspace of the measured data. Some empirical results are included to illustrate the performance advantage of the algorithm compared to standard techniques